Pulmonary or lung cancer is currently a leading cause of cancer death. Early detection of cancer-related pulmonary nodules may provide the greatest chance to prevent deaths due to lung cancer. Non-invasive, high-resolution, thin-slice, multi-slice or multi-detector computed tomography (“CT”) scanners are capable of providing detailed imaging data on anatomical structures. Therefore, non-invasive early detection of pulmonary nodules from CT images holds great promise.
Unfortunately, although vessel-feeding pulmonary nodules are more likely to be malignant than solitary ones, and of important clinical value, their accurate detection from CT images is highly labor-intensive, technically challenging, and requires the careful attention of trained specialists. Existing methods for detecting nodules attached to vessels are typically based on certain geometric and intensity features, such as the circularity and sphericity measures as were reported in, for example, S. G. Armato II, M. L. Giger, J. T. Blackbrun, K Doi, and H. MacMaho, “Three dimensional approach to lung nodule detection in helical CT”, Proc. SPIE Conf. Image Processing, pp.553–559, 1999. These methods have drawbacks and disadvantages in that many false positives may be generated, especially at vessel bifurcation points.